import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from joblib import load

class NN(object):
    def __init__(self):
       self.loaded_model =tf.keras.model.load_model('/NN/my_model.keras')
       self.scaler = load('NN/scaler.joblib') 


    def get_hourly_trand(self):
        n_steps = 7
        df_pivot = pd.read_csv('./NN/scenic_data.csv')
        latest_data = df_pivot.iloc[-n_steps:].values
        latest_data = latest_data.reshape(1, n_steps, latest_data.shape[1])

        predicted = self.loaded_model.predict(latest_data)
        predicted_counts = self.scaler.inverse_transform(predicted)
        predicted_counts[predicted_counts < 0] = 0

        hourly_trend = predicted_counts[0].astype(int).tolist()
        print(hourly_trend)

if __name__ == '__main__':
    n = NN()
    n.get_hourly_trend()